class_docstr: Gaussian Fourier embeddings for noise levels.
class_docstr_tok: ["Gaussian","Fourier","embeddings","for","noise","levels","."]
code: def forward(self, x):
      x = paddle.log(x)
      x_proj = x[:, None] * self.weight[None, :] * 2 * np.pi
      out = paddle.concat(
          [paddle.sin(x_proj), paddle.cos(x_proj)], axis=-1
      )
      return out
  
file_path: PaddleHub/modules/image/text_to_image/stable_diffusion/diffusers/models/embeddings.py
imports: import math
  import numpy
  import paddle
  import paddle.nn
  import paddle.nn.functional
label_desc: Use image node_id 2 for calling the GaussianFourierProjection obj's underlying member method code with example usage: obj.forward(x) and returns: out